The Metadata Objects for Linking Environmental Sciences (MOLES) model has been developed within the
Natural Environment Research Council (NERC) DataGrid project [NERC DataGrid] to fill a missing part of the
¿metadata spectrum¿. It is a framework within which to encode the relationships between the tools used to obtain
data, the activities which organised their use, and the datasets produced. MOLES is primarily of use to consumers
of data, especially in an interdisciplinary context, to allow them to establish details of provenance, and to compare
and contrast such information without recourse to discipline-specific metadata or private communications with
the original investigators [Lawrence et al 2009]. MOLES is also of use to the custodians of data, providing an
organising paradigm for the data and metadata.
The work described in this paper is a high-level view of the structure and content of a recent major revision
of MOLES (v3.3) carried out as part of a NERC DataGrid extension project. The concepts of MOLES v3.3
are rooted in the harmonised ISO model [Harmonised ISO model] - particularly in metadata standards (ISO
19115, ISO 19115-2) and the ¿Observations and Measurements¿ conceptual model (ISO 19156). MOLES exploits
existing concepts and relationships, and specialises information in these standards.
A typical sequence of data capturing involves one or more projects under which a number of activities are
undertaken, using appropriate tools and methods to produce the datasets. Following this typical sequence, the
relevant metadata can be partitioned into the following main sections ¿ helpful in mapping onto the most suitable
standards from the ISO 19100 series.
¿ Project section
¿ Activity section (including both observation acquisition and numerical computation)
¿ Observation section (metadata regarding the methods used to obtained the data, the spatial and temporal sampling
regime, quality etc.)
¿ Observation collection section
The key concepts in MOLES v3.3 are:
a) the result of an observation is defined uniquely from the property (of a feature-of-interest), the sampling-feature
(carrying the targeted property values), the procedure used to obtain the result and the time (discrete instant or
period) at which the observation takes place.
b) an ¿Acquisition¿ and a ¿Computation¿ can serve as the basis for describing any observation process chain
(procedure). The ¿Acquisition¿ uses an instrument ¿ sensor or human being ¿ to produce the results and is
associated with field trips, flights, cruises etc., whereas the ¿Computation¿ class involves specific processing steps.
A process chain may consist of any combination of ¿Acquisitions¿ and/or ¿Computations¿ occurring in parallel or
in any order during the data capturing sequence.
c) The results can be organised in collections with significantly more flexibility than if one used the original
project alone
d) the structure of individual observation collections may be domain-specific, in general; however we are
investigating the use of CSML (Climate Science Modelling Language) for atmospheric data